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Parasuraman Service Excellence Research Prize · Benchmarking Enablers and Results scoring ......
Transcript of Parasuraman Service Excellence Research Prize · Benchmarking Enablers and Results scoring ......
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Parasuraman Service Excellence Research Prize
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Dr. Raed Alrayes
Currently, Dr. AlRayes is lecturing at the Economic and Business Administration College at Al-Imam University in Riyadh. He was appointed their as Assistance Professor on Sep/2006, after he completed his MBA, and PhD from Bradford University in UK. Dr. AlRayes has an excellent background about the financial institutions, after six years of banking experience in a two different banks. Recently, Dr. AlRayes jointed AlRajhi Financial Service -one of the largest investment houses operating in Saudi Arabia- as a consultant in Strategy and Product Development department.
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TableofContentsABSTRACT.................................................................................................................................................. 5
1. INTRODUCTION ................................................................................................................................. 6
1.1 BACKGROUND .........................................................................................................................................61.2 PROBLEMSTATEMENT...............................................................................................................................81.3 RESEARCHQUESTIONS...............................................................................................................................81.4 RESEARCHHYPOTHESES .............................................................................................................................91.5 RESEARCHOBJECTIVES...............................................................................................................................9
2. METHODOLOGY............................................................................................................................... 10
2.1 PHASEONE:ASSESSINGBANKSVIACASESTUDYSTRATEGYUSINGTHEEFQMMODEL..........................................122.2 PHASETWO:ASSESSINGCUSTOMERPERCEPTIONSUSINGTHESERVQUALINSTRUMENT.....................................152.3 PHASETHREE:INVESTIGATIONOFRELATIONSHIPS ........................................................................................16
3. RESULTS .......................................................................................................................................... 17
3.1 QUESTIONNAIREANALYSIS ...............................................................................................................18Profile of Participants................................................................................................................................19Questionnaire Reliability ...........................................................................................................................22Questionnaire Validity ...............................................................................................................................23Identifying Gaps of Service Quality ...........................................................................................................24
Dimension 1: Tangibles.......................................................................................................................................... 25Dimension 2: Reliability ........................................................................................................................................ 25Dimension 3: Responsiveness ................................................................................................................................ 26Dimension 4: Assurance......................................................................................................................................... 26Dimension 5: Empathy ........................................................................................................................................... 27
Aggregate SQ Gaps of participated banks.................................................................................................27Aggregate gap size by dimensions ......................................................................................................................... 27Aggregate gap size for each bank........................................................................................................................... 28
3.2 CASESTUDYANALYSIS.......................................................................................................................28Profile of Participants................................................................................................................................29Size of the Focus Group .............................................................................................................................29Reliability and Validity of the Case Study Approach .................................................................................30Banks Assessment Scores by Criteria .......................................................................................................31
Leadership .............................................................................................................................................................. 31Policy & Strategy ................................................................................................................................................... 32People..................................................................................................................................................................... 32Partnership and Resources...................................................................................................................................... 33Processes ................................................................................................................................................................ 33Customer Results.................................................................................................................................................... 34People Results ........................................................................................................................................................ 34Society Results ....................................................................................................................................................... 35Key Performance Results ....................................................................................................................................... 36
Banks Total Assessment Scores ................................................................................................................36Benchmarking the Aggregate Banks Scores .............................................................................................37
Benchmarking Enablers and Results scoring ratio ................................................................................................. 38Gap Analysis ..............................................................................................................................................40
3.3 ANALYSISOFLAGGINGMEASURES....................................................................................................423.4 HYPOTHESESTESTING .......................................................................................................................45
Pearson Correlation Matrix ......................................................................................................................45
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Testing hypothesis 1...................................................................................................................................48Testing hypothesis 2...................................................................................................................................50Testing hypothesis 3...................................................................................................................................51
3.5 QUALITATIVEDATAANALYSIS ...........................................................................................................52Leadership .................................................................................................................................................52Policy and Strategy ....................................................................................................................................53People Management ..................................................................................................................................55Partnership and Resources Management ..................................................................................................57Process Management .................................................................................................................................59Customer Satisfaction ................................................................................................................................60People Satisfaction ....................................................................................................................................61Society Results ...........................................................................................................................................62Key performance Results ...........................................................................................................................62
4. DISCUSSIONANDCONCLUSION........................................................................................................ 64
4.1 MAINRESEARCHFINDINGS ...............................................................................................................644.1.1 TheEFQMmodelcausalitystructure....................................................................................................... 64a) Enablers correlation with other enablers....................................................................................................... 65b) Results correlation with other Results .......................................................................................................... 66c) Enablers correlation with the Results ........................................................................................................... 674.1.2 ConfirmationofSERVQUALreliabilityandvalidity .................................................................................. 684.1.3 DrivingExcellenceinternallyimpactspositivelyoncustomersperceptionexternally ........................... 694.1.4 CustomersperceptionispositivelycorrelatedwithCustomersresults.................................................. 704.1.5 CustomersresultsispositivelycorrelatedwithKeyperformanceresults ............................................... 704.1.6 SignificanceofVisionandValues............................................................................................................. 71
4.2 MAJORCONTRIBUTIONSOFSTUDY ..................................................................................................72a) Theoretical contributions ......................................................................................................................73b) Methodological contributions................................................................................................................74c) Practical contributions ..........................................................................................................................74
4.3 LIMITATIONSOFSTUDY.....................................................................................................................754.4 FUTURERESEARCHDIRECTIONS........................................................................................................77REFERENCES .............................................................................................................................................78
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ListofTablesTABLE 1: RESEARCH DESIGN OVERVIEW.........................................................................................................12TABLE 2: SERVQUAL SCALE RELIABILITY ANALYSIS....................................................................................22TABLE 4: BANKS GAP SCORES ON TANGIBLES................................................................................................25TABLE 5: BANKS GAP SCORES ON RELIABILITY .............................................................................................25TABLE 6: BANKS GAP SCORES ON RESPONSIVENESS......................................................................................26TABLE 7: BANKS GAP SCORES ON ASSURANCE...............................................................................................26TABLE 8: BANKS GAP SCORES ON EMPATHY ..................................................................................................27TABLE 9 : OVERALL SQ. GAPS SIZE FOR EACH DIMENSION ............................................................................27TABLE 10: OVERALL SQ. GAP SCORES FOR EACH BANK .................................................................................28TABLE 11: AN OVERVIEW OF THE FOCUS GROUP INTERVIEWS (DATES, AND PLACE) ...................................28TABLE 12: SIZE OF THE FOCUS GROUP ............................................................................................................30TABLE 13: GAP ANALYSIS..................................................................................................................................40TABLE 14: DEFINING THE CORRELATION STRENGTH OR WEAKNESS ..............................................................46TABLE 15: PEARSON CORRELATION MATRIX AMONG ALL VARIABLES ..........................................................47TABLE 16: PEARSON CORRELATION MATRIX FOR THE TESTED VARIABLES ..................................................48TABLE 17: REGRESSION ANALYSIS FOR HYPOTHESIS H1.................................................................................49TABLE 18: RELATIONSHIP BETWEEN CEFS AND SQ GAP ................................................................................49TABLE 19: REGRESSION ANALYSIS FOR HYPOTHESIS H2.................................................................................50TABLE 20: RELATIONSHIP BETWEEN SQ GAP AND CUSTOMERS RESULTS ....................................................50TABLE 21: REGRESSION ANALYSIS FOR HYPOTHESIS H3.................................................................................51TABLE 22: RELATIONSHIP BETWEEN CUSTOMERS RESULTS AND KEY PERFORMANCE RESULTS ...............51TABLE 23: ENABLERS CORRELATION WITH OTHER ENABLERS .......................................................................65TABLE 24: RESULTS CORRELATION WITH OTHER RESULTS ............................................................................66TABLE 25: ENABLERS CORRELATION WITH THE RESULTS ..............................................................................67TABLE 26: THE AGGREGATE RANKS OF ALL BANKS .........................................................................................71
ListofFiguresFIGURE 1: RESEARCH PROCESS .......................................................................................................................11FIGURE 2 : STRUCTURE OF PHASE ONE ...........................................................................................................14FIGURE 3: PHASE TWO.......................................................................................................................................15FIGURE 4: PHASE THREE ...................................................................................................................................17FIGURE 5: BANKS SHARES OF COMPLETED QUESTIONNAIRES ......................................................................18FIGURE 6 : RESPONDENTS DISTRIBUTION BY GENDER ....................................................................................19FIGURE 7 : PARTICIPANTS BY EDUCATIONAL QUALIFICATION .......................................................................20FIGURE 8: PARTICIPANTS BY NATIONALITY.....................................................................................................20FIGURE 9: PARTICIPANTS BY PROFESSION .......................................................................................................21FIGURE 10: PARTICIPANTS BY INCOME (IN SR) ...............................................................................................21FIGURE 11: METHODS OF COMMUNICATION BETWEEN CUSTOMERS AND BANK ..........................................22FIGURE 12: DISTRIBUTION OF FOCUS GROUP INTERVIEWEE BY PROFESSIONAL STATUS ...............................29FIGURE 13: PARTICIPANTS WORK EXPERIENCE IN YEARS ..............................................................................29FIGURE 14: BANKS ASSESSMENT SCORES FOR LEADERSHIP CRITERION........................................................32FIGURE 15: BANKS ASSESSMENT SCORES FOR POLICY AND STRATEGY CRITERION......................................32FIGURE 16: BANKS ASSESSMENT SCORES FOR PEOPLE CRITERION................................................................33
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FIGURE 17: BANKS ASSESSMENT SCORES FOR PARTNERSHIP AND RESOURCES CRITERION .........................33FIGURE 18: BANKS ASSESSMENT SCORES FOR PROCESSES CRITERION..........................................................34FIGURE 19: BANKS ASSESSMENT SCORES FOR PROCESSES CRITERION..........................................................34FIGURE 20: BANKS ASSESSMENT SCORES FOR PEOPLE RESULTS CRITERION................................................35FIGURE 21 : BANKS ASSESSMENT SCORES FOR SOCIETY RESULTS CRITERION .............................................35FIGURE 22 BANKS ASSESSMENT SCORES FOR KEY PERFORMANCE RESULTS CRITERION ............................36FIGURE 23: BANKS TOTAL ASSESSMENT SCORES ............................................................................................37FIGURE 24: AGGREGATE SCORE REPORTED BY PARTICIPATED BANKS ..........................................................38FIGURE 25: BANK SCORES RATIO ON ENABLERS AND RESULTS CRITERIA......................................................38FIGURE 26: EQA SCORES RATIO ON ENABLERS AND RESULTS CRITERIA.......................................................39FIGURE 27: BANKS MEAN SCORES VS. THE EQA MEDIAN SCORES .................................................................39FIGURE 28 :GAP ANALYSIS ................................................................................................................................41FIGURE 29 AVERAGE ANNUAL GROWTH OF THE BANKS SHARE PRICES .........................................................42FIGURE 30: AVERAGE ANNUAL GROWTH OF THE BANKS NET PROFIT...........................................................43FIGURE 31: AVERAGE ANNUAL GROWTH OF THE BANKS ROA ......................................................................43FIGURE 32:AVERAGE ANNUAL GROWTH OF THE BANKS OWNERS EQUITY ..................................................44FIGURE 33 AVERAGE ANNUAL GROWTH OF THE BANKS ROE........................................................................45
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ABSTRACT
This empirical research sought to investigate the Critical Excellence Factors (CEFs) that
drive Excellence in banking industry. Moreover, it examines whether customers perceive
the service of an excellent bank differently from a less-excellent bank.
Three hypotheses were formed then tested through case study and survey strategy
(triangulation), within the Saudi banking industry context. The study combines the EFQM
excellence model as an internal assessment tool (case studies), with the SERVQUAL gap
model for external assessment (questionnaires). Analysing and contrasting the two sets of
results allowed the study to achieve its main objectives. Based on the empirical work, the
study identifies several CEFs that must be carefully considered when driving excellence in
banking.
Keywords: Service Quality, Service Excellence, EFQM Excellence Model, SERVQUAL,
Banking, Empirical Study.
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1. INTRODUCTION
1.1 Background
In many studies, Service Quality (SQ) has been correlated with customer satisfaction within
the banking industry (LeBlanc and Nguyen 1988; Blanchard and Galloway 1994). In
addition, successful service organisations are making a great effort to achieve Service
Excellence (SE). Satisfying all stakeholders is considered one of the fundamentals within
many excellence models, for example, European Foundation for Quality Management
(EFQM) and Malcolm Baldrige National Quality Award (MBNQA). However, banks now
know that delivering quality service to customers is essential for success and survival in
todays global and competitive banking environment (Wang et al. 2003). There are two
main strands of literature in which theories and models related to SE have been developed.
They are: a) Service Marketing; and b) Total Quality Management (TQM) literature. Each
pool of literature tackles the SE topic from a different perspective. In addition, each area of
literature has its potential advantages and drawbacks.
The Service Marketing literature reveals that excellence in service provision, and partially in
the context of banks, is generally based on measurement of outcomes. Particularly, through
the extensive use of the SERVQUAL gap model (Angur et al. 1999; Lassar et al. 2000;
Newman 2001; Arasli et al. 2005), or else through the adaptation of the SERVQUAL (Bahia
and Nantel 2000; Aldlaigan and Buttle 2002). Basically, SERVQUAL gap model
(Parasuraman et al. 1988) is a 22-item Likert scale survey instrument, which compares
customer expectations and perceptions regarding five attributes of SQ: Tangibles,
Reliability, Responsiveness, Assurance, and Empathy.
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The TQM literature has also contributed significantly in developing theories and models for
quality enhancement in service organisations. For example, the EFQM Excellence model is
a continuation within the TQM literature that has been influential in improving SE. The
EFQM Excellence Model was described as a practical tool that helps organisations in
establishing an appropriate management system by measuring where they are on the path to
Excellence, helping them to understand the gaps, and then stimulating solutions (EFQM
2003).
The EFQM model has much strength. First, the model provides a holistic approach to
achieving SE. Second, the model focuses on satisfying all the companys stakeholders rather
than only customers or shareholders. Third, the strength of the model is apparent since it is
related to the financial results. So the model indirectly includes shareholders interests, but
more importantly it helps the intuitive understanding of what is needed to improve the
financial result. Finally, the EFQM Excellence model is a strategic internal assessment tool,
based on the TQM principles, that helps in clearly identifying any causes of service gaps.
Moreover, it ensures that SQ is delivered continually through its strategic philosophy.
Nonetheless, the EFQM model has several limitations. First, there are is little literature and
few empirical studies on this model, as a result of its short life. Second, the EFQM model is
based on a sound and logical causal structure since it is stipulated by the EFQM that there
is a causal relationship between enablers and results (Ghobadian and Woo, 1996). However,
there is a lack of evidence supporting the causal structure. Black and Porter (1996) argue
that many quality award-based frameworks were not developed using a scientific approach,
based on the identification and validation of Critical Success Factors [CSFs] but rather
mainly result from the assembling of ad hoc evidence and successful case stories. They are
not based on systematic empirical evidence.
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1.2 Problemstatement
Only a few studies have attempted to test the causal structure that underpins the EFQM
excellence model. A few researchers have directed their investigation towards this matter
(e.g. Ghobadian and Woo 1996; Dijkstra, 1997; Eskildsen and Dahlgaard, 2000; Esquildsen
et al., 2001; Eskildsen et al., 2000; Bou-Llusar et al., 2005). However, the empirical
research on the causal relationships within the EFQM excellence model is still limited, since
it is mostly based on studies that test isolated associations.
On the other hand, the issue SQ in the retail banking environment has been the focus of the
Service Marketing literature. The literature in this area reveals that excellence in service
provision, and particularly in the context of banks, is generally based on:
measurement of outcomes, extensive use of SERVQUAL, and no linkages between internal processes.
Based on the review of the two sources of literature, and the limitation of the models
retrieved in this respect (EFQM and SERVQUAL), the researcher formulated some research
questions as follow.
1.3 Researchquestions
From the research problem, five research questions emerged.
What are the main elements that drive and sustain excellence in banking, what can
be called the Critical Excellence Factors (CEFs)?
What are the relationships among these CEFs?
Do customers perceive excellent bank that apply CEFs differently?
In what way do the CEFs affect the banks Key performance Results?
What are the benefits and obstacles to implementing these CEFs in the bank?
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1.4 Researchhypotheses
To answer the previous questions, three hypotheses were formed, derived from the review
of the literature:
H1: An organisation that drives excellence internally (i.e. Enabler criteria) impacts positively on customers perception externally (i.e. on the SERVQUAL gap).
H2: Customers perception (i.e. SERVQUAL score) is positively correlated with the assessment score of the Customers result criterion within the EFQM model.
H3: Organisations that attract positive customer perceptions externally will have healthier financial results. In other words, there is a positive relationship between the SERVQUAL gap size and the Key performance result criterion through the Customer result criterion.
1.5 Researchobjectives
This study mainly aims to explore and identify the major CEFs within the banking industry.
Furthermore, it will investigate the dynamic relationships among CEF elements. Finally, it
will measure the impact created by the CEFs on customers perceptions. The study had the
following primary objectives:
To study the relevance of SQ and Excellence in the banking industry.
To identify the CEFs that affect SE in the financial services.
To discuss various models of SQ and Excellence and to identify common generic
factors.
To compare key drivers of Service Excellence and key enablers that impact using
a sample of different banking institutions (qualitative). Further, to determine how
good they are in driving Excellence.
To evaluate the conceptual aspects of service provision from a user point of view
(using SERVQUAL).
To investigate how driving excellence internally is correlated to customers
perception externally.
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2. METHODOLOGY
This study represents exploratory and explanatory empirical research that aims to verify
existing practices of SE in Saudi banking industry. Therefore, measurement of "what",
"how" and "why" are required to understand the process of driving excellence. The "what"
aspects of research necessitate the use of quantitative methods, while the "how" and "why"
require qualitative methods.
In this study, a methodological triangulation approach, which combines quantitative and
qualitative methods, was adopted through a complementary use of secondary data,
questionnaire survey, and case studies. The use of secondary data as a part of this research;
aims to verify the finding of this study.
A multi-methods strategy was adopted in this empirical research, following frequent
recommendations from authoritative researchers to use multiple, complementary methods,
since each of these methods has advantages and disadvantages (Ott, 1989). Reichers and
Schneider (1990) argued that such a step may yield the most valid measure. In addition,
Flick (1992) and Luccini (1996) argued that the triangulation data gathering method allows
the researcher to obtain a complex picture of the phenomena being studied, which might
otherwise be unavailable if only one method were utilised. In this study, a triangulation
method, a combination of survey administration, focus group interview and documentation
analysis was used. Figure 1, shows the entire process of this study.
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Figure 1: Research Process
As Yin (2003a) stated, research design is a blueprint of the research. Thus, research
design covers strategic decisions concerning the choice of data collection methods, and
more tactical decisions regarding measurement and scaling procedures, questionnaire,
samples and data analysis (Zikmund, 2003). Table 1, illustrates the design of this study.
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Table 1: Research Design Overview
ResearchPurpose Exploratory+Explanatory
TriangulationResearchApproach
Qualitative+Quantitative Quantitative
ResearchStrategy Casestudy Survey
AnalysisUnit Bank Bankscustomer
ModelsUsed EFQM(LadderofExcellence) SERVQUAL
DataType Primary Secondary Primary
DataCollectionMethodsFocusgroupInterviews
Directobservation
Documents
Archivalrecord
Questionnaire
TypeofGeneratedData Qualitative&Quantitative Quantitative Quantitative
The subsequent sections will discuss in depth the above researchs plan and design. In fact,
due to the various objectives in this empirical study, the study was divided into three phases.
Each phase will be discussed and described independently.
2.1 PhaseOne:AssessingbanksviacasestudystrategyusingtheEFQMmodel
Juran (1995) argued that there is a paucity of systematic and rigorous evaluation in many
of the quality studies. Furthermore, Wilson and Durant (1994) state the need for more
theory grounded and contingency based research rather than the research being restricted to
deductive approaches. It is suggested that a methodology, which inquires more deeply into
TQM-related events within the organisation, is needed. This will enable a coherent and
firmly founded set of TQM theories to be elucidated (Leonard and McAdam, 2001). Based
on these recommendations; primarily a case study strategy was selected to fulfil this studys
objectives.
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The case study strategy included a focus group interviews which aimed to explore how
banks are driving excellence internally, and what are the common and distinctive elements
of excellence among them. The data gathered from the focus group interviews were
completed by several archival records and a number of the internal banks documents (e.g.
evidence supporting the identified CEFs). Mainly, the case study strategy generated two
types of data. First, qualitative data is representing the strengths and weaknesses of each
bank against the identified CEFs; second, quantitative data demonstrating the final
assessment score obtained by each bank against the nine EFQM criteria.
In this study, the EFQM excellence model was deployed to generate not only quantitative
data but also qualitative. Such an approach is supported by Gummesson (1998), who
observed, There is a need for inductive research that allows reality to tell its own full story
without forcing received theory on it. This study therefore sought to add some empirical
insights to the theoretical literature on Service Excellence through exploring how well major
Saudi banks are driving excellence. Specifically, this phase sought to identify the Critical
Excellence Factors applied by participant banks. In this phase the EFQM model (Ladder of
Excellence) was employed as a structural diagnostic tool that facilitated achieving this
phases objectives which were:
To investigate how banks are driving excellence and what are the generic and common elements of excellence
To determine how good they are in driving Excellence
Construct validity deals with the use of instruments and measures that accurately measure
and operationalise the constructs of interest in a study. Because most instruments and
measures are not necessarily as accurate as would be desired, a common strategy is to use
multiple measures of the same constructs as part of the same study. Both internal and
external validity were touched on in the discussion on the role of theory. Internal validity
can be achieved through the specification of the units of analysis, the development of a
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priori rival theories, and the collection and analysis of data to test these rivals. Similarly, one
can achieve external validity through the specification of theoretical relationships, from
which generalisations can then be made.
A further confirmatory step the researcher provided for the research findings was to match
the EFQM assessment scores of the results for each bank, alongside its Key Performances
Indicator (KPI) as lagging measures. The overall structure of the first phase is illustrated in
the next Figure 2.
Figure 2 : Structure of Phase One
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2.2 PhaseTwo:AssessingcustomerperceptionsusingtheSERVQUALinstrument
Phase two intends to evaluate customers perceptions externally, in order to investigate the
impact of driving excellence internally. A Likert-scale questionnaire survey was conducted
to measure customers perceptions based on the SERVQUAL structure, which is a 22-item
Likert scale survey that compares customer expectations and perceptions regarding five
attributes of service quality Basically, the instrument was implemented straightforwardly as
in many former studies (Angur, Nataraajan et al., 1999; Lassar, Manolis et al., 2000; Arasli,
Mehtap-Smadi et al., 2005). (see Figure 3).
Figure 3: Phase two
It is vital to mention here that the SERVQUAL questionnaire was developed originally in
English. Since English is not the first language in Saudi, it was recognised that some of the
respondents would not be familiar with the original questionnaire language. Thus, the
questionnaire was translated into Arabic to avoid miscommunication and misinterpretation.
Choosing a person to translate a questionnaire was an important issue. In this study, a
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translator was carefully selected using two criteria. First, the person must have a good
understanding of both English and Arabic languages; more importantly, the person must
also be capable of writing high-standard, official Arabic, especially that which is suitable for
a questionnaire. Secondly, the persons credentials must show that he or she has extensive
experience in developing questionnaires in Arabic. In this study, an Arab native, who is
fluent in English, translated the questionnaire into Arabic. This person has had more than 5
years of experience in conducting both management and academic research in Arabic. The
final version of the questionnaire and its translated version are presented as (appendix -B).
2.3 PhaseThree:InvestigationofRelationships
According to Robson (1993) the purpose of an enquiry may change over time that means a
study may include more than one purpose. This phase was intended, unlike the previous
ones, to look into the causality effects, as the fundamental research concern was with
establishing causal connections between the independent variables and dependent variables,
rather than mere relationships between them, in order to test the third hypothesis. The
independent variables are those which have a causal impact on the dependent variables
(Bryman, 1992). The dependent variable is normally the construct of primary interest to the
researcher (Nachmias and Nachmias, 1996), or the variables which the researcher wishes to
explain (Cooper and Schindler, 1998). The independent variables identified for this research
were the banking related CEFs, and the dependent variable was the customers perception of
service quality. Figure 4, illustrates the structure of phase 3, and it indicates how critical
output data from the previous phases fed into this phase.
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Figure 4: Phase three
In this phase, Correlation and multiple regression were utilised to examine the suggested
causality structure findings are demonstrated on result section.
3. RESULTSThis section contains an analysis of the five banks assessment scores, customers survey,
and collected lagging performance figures. First, it will depicts the results of SERVQUAL
questionnaire administration, including a demographic profile of participants, and the
verification of the validity and reliability of the research instrument. Second, the analysis of
the quantitative (EFQM) data generated by the applied case study strategy (i.e. the
assessment of the five participating banks) with descriptive results of the focus group
interviews were outlined. Further, benchmarking exercise for all banks scores is carried out.
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Third, the analysis of the lagging measure of all participating banks are analysed to
determine each banks position.
The data collected are analysed using the SPSS package, along with standard statistical
analysis techniques: Factor analysis, Pearson correlation, and Regression analysis
3.1 QUESTIONNAIREANALYSISThe questionnaire consisted of three parts. The first part consisted of the respondents
profile. The second measured the respondents expectations when dealing with an excellent
bank. Finally, the third part measures the respondents perceptions about the SQ of their
bank.
A total of one thousand questionnaires were equally distributed, 200 for each banks
customers, through a systematic sampling method to ensure that only participating banks
customers were reached. Of the 1000 circulated questionnaires, 613 (61.3%) were collected.
Only 31 questionnaires were not completed. Hence, the valid sample was 582 (58.2%).
Figure 5, illustrates every banks share of those valid questionnaires.
Figure 5: Banks Shares of Completed Questionnaires
21%
23%
18%
16%
23%
Bank-A Bank-B Bank-C Bank-D Bank-E
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The above figure demonstrates similar level of response in each bank, with each accounting
for from 16% to 23% of the final total. Data from the two parts of the questionnaires (Part
A: Customers Expectation, and B: Customers perception) were entered into the Statistical
Package for the Social Science, SPSS version11.0, and MS-Excel 2003 for analysis
purposes. The following section will present respondents demographic information, in
terms of gender, level of education, nationality, profession, monthly income, and channels
by which they communicated with their bank.
Profile of Participants
The next figure 6 shows the distinction of participants by their gender. Male represented
73%, a total of 424 male participants, while females represented 158, 27% of the total
population.
Figure 6 : Respondents distribution by gender
27%
73%
Female Male
The next figure 7 shows participants by their educational qualifications. Bachelor degree
holders represented the majority, since they were about 55% of the population. Post-
graduates and high school certificate holders represented 34% and 8% respectively. Only
4% the participants had a lower qualification are representing only
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Figure 7 : Participants by Educational Qualification
4%
8%
54%
23%11%
Less than High school High school Bachelor's Master PhD
Figure 8 exhibits contributors by their nationality. Saudis represented 60%, while Non-
Saudis represented 40% of the total population.
Figure 8: Participants by Nationality
60%
40%
Saudi Non-Saudi
Regarding participants by their occupation. Figure 9 illustrates that the majority of
participants were either Government employees or private-sector employees; representing
correspondingly 31% and 27% of the total sample. Entrepreneurs came next representing
17%. Students and other are represented 19% and 6% respectively.
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Figure 9: Participants by Profession
31%
27%
17%
19%
6%
Gov. employee Privet sector employee Entrepreneur Student Other
The next Figure 10 demonstrates participants by their monthly income. The second group
(i.e. from SR 5,000-1,000) is considered as the average group. Accordingly, only 15% of the
participants had below average income, whilst 37% were within this group and around 48%
were above the population average.
Figure 10: Participants by Income (in SR)
15%
37%29%
13%6%
< 5,000 5,000-10,000 10,000- 20,000
20,000 -50,000 > 50,000
The last personal data collected about participants concerned the channels they used to
communicate and deal with their banks. Figure 11 illustrates the leading position maintained
by the two traditional channels (i.e. branch visit and ATM) since they were ticked by more
than 90% of the sample.
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Figure 11: Methods of Communication Between Customers and Bank
46%
59%
90%
91%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
On-line Banking
Phone Banking
Visiting Branch
ATM
On-line Banking Phone Banking Visiting Branch ATM
Telephone banking was revealed to be the next preferred channel, since about 60% of the
population marked it. Internet banking was used by 46% of the sample.
Questionnaire Reliability
Reliability refers to the instruments ability to demonstrate overall consistency as well as
internal consistency among items within each of the five theorized dimension. The alpha
coefficient is a measure for assessing the internal consistency of both survey parts
(Expectations and Perceptions items) for each dimension.
The reliability of all five dimensions was examined for each item individually (total of 22
items) using reliability test program of SPSS. Findings on table 2 revealed that the used
instruments formed a cohesive scale; the test findings are demonstrated.
Table 2: SERVQUAL scale reliability analysis
Reliability Coefficients (Alphas)
SQ Dimension Number of items Expected SQ Perceived SQ
Tangible 4 .932 .911
Reliability 5 .750 .723
Responsiveness 4 .668 .738
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Assurance 4 .870 .859
Empathy 5 .736 .776
Total Scale Reliability 22 .918 .927
The Cronbach's alpha reliability coefficients for the five SERVQUAL dimensions are
similar across previous studies (e.g. Carman, 1990; Finn and Lamb, 1991; Babakus and
Boller, 1992; Babakus and Mangold, 1992; Headley and Miller, 1993; Taylor and Cronin,
1994) The lowest reliability is 0.59 reported by Finn and Lamb (1991) and the highest
reliability is 0.97 reported by Babakus and Mangold (1992).
Regarding the expectation items, the reliability coefficient for each of the five dimensions
ranged from .668 to .932. The Tangible dimension achieved the highest alpha (.932),
followed by Assurance (.870), Reliability (.750), Empathy (.736) and Responsiveness
(.668).The reliability coefficient for each of the five dimensions for the perception items
ranged from .911 to .723. Tangibles again scored the highest alpha (.991), followed by
Assurance (.859), Empathy, (.776) Responsiveness (.738) and Reliability (.723). Thus, the
gathered data are consider to be reliable, since its reported coefficient figures fall within the
range of .93 to .53 that has been reported in the service quality literature. Moreover, all
items received very high alpha scores, ranging from 0.668 to 0.932, and were well above the
generally accepted lower limit of 0.7 (Hair et al., 1995).
Questionnaire Validity
The following two subsections illustrate how the used SERVQUAL questionnaire was
proven to be a valid instrument, using three different methods: face and content validity, and
construct (convergent) validity.
Face validity is the mere appearance that a measure is valid (Kaplan and Sacuzzo, 2005). In
other words, face validity is a subjective criterion which reflects the extent to which scale
items are meaningful and appear to represent the construct being measured (Parasuraman et
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24
al. 1991). Content validity is the degree to which the instrument provides an adequate
representation of the conceptual domain that it is designed to cover. Apart from face
validity, content validity is the only type of validity for which the evidence is subjective and
logical rather than statistical (Kaplan and Sacuzzo, 2005). Content validity refers to the
degree which an instrument covers the meaning of the concepts included in a particular
research (Babbie, 1992).
For this study, the content validity of the proposed instrument was adequate, because the
instrument had been carefully constructed, validated and refined by PZB supported by an
extensive literature review. Moreover, the questionnaire were passed by researcher to six
specialists in this area, who were requested to review the questionnaire and determine the
suitability and difficulty of the questions. The final questionnaire version then followed their
comments and suggestions. Additionally, considering the advice of Nunnally (1978) that
any pre-test must be carried out on a similar group, the researcher completed a pilot-study
by distributing the questionnaire to a similar target audience. 30 people were asked if the
completion of the questionnaire created any difficulties. All respondents replied that they
faced no problems in completing the questionnaires. Hence the instrument can be
considered to have face and content validity.
Identifying Gaps of Service Quality
As revealed earlier, the SERVQUAL method was used to identify quality gaps by
calculating difference in expectation and perception scores between the 22 statements. A
negative score indicated the existence of a service quality gap, where the customers were not
having their expectations met by their banks. The following subsections will demonstrate
the analysis of the gathered data.
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Dimension 1: Tangibles
Tangibles are about the appearance of physical facilities, equipment, personnel, and
communication materials. Table 4, demonstrates to what extent participating banks do
successfully met expectations about their tangibles. Calculating the mean for this dimension
revealed that bank B and A exceeded customer expectations in this criterion. Conversely,
banks C, D, and E did not meet their customers expectations. However bank D was very
close to its customers expectation.
Table 3: Banks Gap Scores on Tangibles
Bank-A Bank-B Bank-C Bank-D Bank-E
Q1 0.4 0 -1.5 0.3 -2
Q2 0.7 0.5 -1.9 -0.4 -3
Q3 -0.16 -0.3 -1.9 -0.6 -1.4
Tang
ible
s
Q4 0.3 -0.1 -1.6 -1 -1.9
Mean 1.24 0.1 -6.9 -1.7 -8.3
Dimension 2: Reliability
Reliability means the ability to perform the promised service dependably and accurately.
Table 5, indicates that all participating banks fell below their customers expectations.
Reliability seems to be the greatest gap identified by banks customers (as will be explained
later). However, banks D, and A had lower gap scores, ranging from -5 to -7, while the rest
had scores in excess of -10.
Table 4: Banks Gap Scores on Reliability
Bank-A Bank-B Bank-C Bank-D Bank-E
Q5 -1.6 -1.9 -2.84 -0.8 -3.9
Q6 -1.3 -1.7 -3.68 -0.7 -4.25
Q7 -0.16 0.1 -0.9 0.6 -1.55
Q8 -1 -2.3 -3.7 -2.7 -4.7 Rel
iabi
lity
Q9 -2.7 -4.1 -2.6 -1.5 -3.9
Mean -6.76 -9.9 -13.72 -5.1 -18.3
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Dimension 3: Responsiveness
Responsiveness is about the willingness to help customers and provide prompt service. Only
bank A reported a positive gap on this dimension. In contrast, all other banks were below
their customers expectation. However, bank B was not so far from its customers
expectations, with a negative gap of -1.1 only, as illustrated in Table 6.
Table 5: Banks Gap Scores on Responsiveness
Bank-A Bank-B Bank-C Bank-D Bank-E
Q10 0.4 1 -0.4 -0.8 -1
Q11 -0.3 1 -1.8 -0.7 -3
Q12 0.5 -1 -2.9 -3.4 -2.4
Res
pons
iven
ess
Q13 0.3 -2.1 -1.6 -3.5 -2.9
Mean 0.9 -1.1 -6.7 -8.4 -9.3
Dimension 4: Assurance
Assurance is about the knowledge and courtesy of staff and their ability to convey trust and
confidence. Bank A, B and D had the smallest gaps regarding assurance only - 0.1, -2.2, and
-3.2. In contrast, banks C and E had relatively large negative gaps on this dimension, -9.2,
and -14.8 respectively as illustrated in Table 7.
Table 6: Banks Gap Scores on Assurance
Bank-A Bank-B Bank-C Bank-D Bank-E
Q14 -0.6 -1 -2.4 -0.8 -4
Q15 0 -0.9 -3.8 0 -5
Q16 0.3 0.2 -1.9 -1.4 -2.4
Ass
uran
ce
Q17 0.2 -0.5 -1.1 -1 -3.4
Mean -0.1 -2.2 -9.2 -3.2 -14.8
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Dimension 5: Empathy
Empathy means caring and individualised attention to the customer. Table 8, illustrates that
banks A, D, and B reported the smallest negative gaps, -1.4, -3.3 and -4 respectively. Banks
C, and E were far way a from their customers expectations -6.9, and -11.3 respectively.
Table 7: Banks Gap Scores on Empathy
Bank-A Bank-B Bank-C Bank-D Bank-E
Q18 0.4 -1.5 -1.4 0.2 -2
Q19 -3 -2 -2 -3 -4
Q20 1.4 0 -1.9 -1 -1.4
Q21 -0.5 0 -1 0 -2 Empa
thy
Q22 0.3 -0.5 -0.6 0.5 -1.9
Mean -1.4 -4 -6.9 -3.3 -11.3
Aggregate SQ Gaps of participated banks
The previous subsections sought to represent the position of each participating bank
regarding each dimension. However, it is vital to draw the overall picture of all banks as one
bulk, in order to achieve the main objectives of this study.
Aggregate gap size by dimensions
As illustrated in Table 9, participant banks overall gap scores were negative on all five
dimensions. In particular, the table shows a relatively large negative gap on the reliability
dimension (-10.75). Other dimensions, i.e. Tangibles, Responsiveness, Assurance, and
Empathy, had lower negative gap scores -3.11, -4.92, -5.9, and -4.68 respectively.
Table 8 : Overall SQ. gaps size for each dimension Tangibles Reliability Responsiveness Assurance Empathy
Mean for all five bank -3.11 -10.75 -4.92 -5.9 -4.68
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Aggregate gap size for each bank
The second hypothesis was that driving excellence internally impacts positively on
customers externally. Therefore, scores were calculated for customers overall perceptions
about their bank (Table 10). Obviously, all five banks fell short of their customers
expectations. Nevertheless, Bank A was very close to meeting its customers expectations,
with a gap score of only -5.62. Banks B and D came in second place with -17.1, and -21.7
respectively. Finally, banks C, and E were far away from their customers expectations they
scored -42.42, and -60 respectively.
Table 9: Overall SQ. Gap scores for each bank Bank-A Bank-B Bank-C Bank-D Bank-E
The total of all five Gaps -5.62 -17.1 -42.42 -21.7 -60
3.2 CASESTUDYANALYSISFive focus group interviews were conducted during 2006; on 14th Feb, 10 th & 27 th Mar, and
11 th & 26 th April. The first two focus interviews were held during working time at a special
meeting room on each banks headquarters. However, because of the numerous
interruptions by the participants subordinates and colleagues during meetings, the
researcher arranged for an alternative external location to ensure participants focus on the
interview programme without any distraction. The business centre at Riyadh Marriot
Hotel was the venue where the last three focus groups were held (see Table 11). Finally, to
guarantee the promised anonymity, every participating bank was given a code from A-E,
and only the researcher knows each bank code.
Table 10: An overview of the Focus Group Interviews (Dates, and Place)
No. Date Venue Number of participants 1 14/Feb/06 Banks Headquarter 8 2 10/Mar/06 Banks Headquarter 7 3 27/Mar/06 Business Centre (Marriot Hotel- Riyadh) 9 4 11/April/06 Business Centre (Marriot Hotel- Riyadh) 8
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5 26/April/06 Business Centre (Marriot Hotel- Riyadh) 7 Total Participants 39
Profile of Participants
The distribution of the focus group participants and by professional status and work
experience are presented in Figure 12 and Figure 13.
Figure 12: Distribution of focus group interviewee by professional status
26%
59%
15%
Top Manag Medium Manag Low Manag
Figure 13: Participants work experience in years
28%
49%
23%
Less than 5 5 to 10 More than 10
Size of the Focus Group
The numbers of participants also play an important role in the focus group interview. In
general, there are about six to ten participants in one focus group session, but some sessions
may have up to twelve people (Stewart and Shamdasani, 1990; Morgan, 1997). Dawson et
29
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30
al. (1993) pointed out that focus groups work well with four to twelve people. The
information gained may not be adequate or rich enough if there are fewer people to interact.
In this study, in order to gain sufficient information, ten employees from each bank were
invited in advance; i.e. a total of 50 employees were recruited. However, 39 participants (78
%) took part in the five focus groups (see table 12).
Table 11: Size of the Focus Group
Bank code
Number of participants
A 8 B 7 C 9 D 8 E 7
Total 39
Reliability and Validity of the Case Study Approach
The most important issues related to the case study design are to create designs with
construct validity, internal validity, external validity, and reliability (Yin, 2003b). This
study applied the EFQM Ladder of Excellence as a formal case study protocol, since it
offered a huge structural support. The researcher deployed the structured model in order to
obtain valid and reliable quantitative and qualitative data. Such an approach is supported by
Gummesson (1998), who observed There is a need for inductive research that allows
reality to tell its own full story without forcing received theory on it. This study therefore
sought to add some empirical insights to the theoretical literature on Service Excellence
through exploring how well major Saudi banks are driving excellence. Specifically, this
phase sought to identify the CEFs applied by participant banks. In this phase the EFQM
model (Ladder of Excellence) is employed as a structural diagnostic tool that facilitated
achieving this phases objectives which are:
To investigate how banks are driving excellence and what are the generic and
common elements of excellence?
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31
To determine how good they are in driving Excellence?
Construct validity deals with the use of instruments and measures that accurately measure
and operationalise the constructs of interest in a study. Because most instruments and
measures are not necessarily as accurate as would be desired, a common strategy is to use
multiple measures of the same constructs as part of the same study. Both internal and
external validity were touched on in the discussion on the role of theory. Internal validity
can be achieved through the specification of the units of analysis, the development of a
priori rival theories, and the collection and analysis of data to test these rivals. Similarly, one
can achieve external validity through the specification of theoretical relationships, from
which generalisations can then be made. A further confirmatory step the researcher provided
of the research findings was to match the EFQM assessment scores of the results for each
bank, alongside its KPIs as lagging measures.
Banks Assessment Scores by Criteria
The subsequent subsections will report the score obtained by each bank for the nine
excellence criteria. As known, the EFQM Excellence model gives a different weight to each
criterion. Therefore, this sections figures will to take this into account by setting the
highest value of the (y) axis in each figure according to the maxim score of the represented
criterion.
Leadership
All participants excluding bank E reported comparable scores, ranging from 40 to 54 points,
about half or less the maximum score for the leadership criterion (see Figure 14). Bank E
was far away from the rest of the banks, scoring only 20 points.
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Figure 14: Banks assessment scores for Leadership criterion
Leadership
4050
40
20
54
0
10
20
30
40
50
60
70
80
90
100
A B C D E
Policy & Strategy
Regarding the Policy and Strategy criterion, banks A, B, and C reported relatively higher
scores (52, 68, and 62) out of 80 respectively. On the other hand, banks D and particularly E
obtained very low scores 35 and 20 respectively, (see Figure 15).
Figure 15: Banks assessment scores for Policy and Strategy criterion
Policy & Strategy
52
6862
35
20
0
10
20
30
40
50
60
70
80
A B C D E
People
On managing people, banks scores appeared to be more varied Bank B was the best scoring
with 75.4 points, banks C and A came next scoring 59 and 48 respectively. As usual, banks
E and D scored the lowest regarding this criterion (39, and 23.63 points). Remarkably, this
was the first time that bank E was not the lowest scoring bank among participants (see
Figure 16).
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Figure 16: Banks assessment scores for People criterion
People
48
59
39
75.4
23.63
0
10
20
30
40
50
60
70
80
90
A B C D E
Partnership and Resources
Figure 17, shows that bank A, B, and D, out of 90 points; scored the highest, 65, 61, and 56
respectively. Banks C and E reported lower scores (46 and 34) on managing people.
Figure 17: Banks assessment scores for Partnership and Resources criterion
Partnership and Resources
4656
34
6165
0
10
20
30
40
50
60
70
80
90
A B C D E
Processes
Clearly, Figurer 18 illustrates a disappointing situation among the five Saudi banks. Banks
C and E scored only about 25 out of 140. Comparatively banks A, B and D had healthier
scores (53, 52 and 45). Still, all scores were far away from the acceptable levels.
33
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Figure 18: Banks assessment scores for Processes criterion
Processes
24
53 52
25
45
0
20
40
60
80
100
120
140
A B C D E
Customer Results
Customers results is the first Results criterion. Figure 19 illustrates that Bank B obtained a
high score of 153 points out of 200. Next was bank A with 104 points. Bank C and D scored
66 and 86 respectively. As usual, bank E had the lowest score, only 52 points out of 200.
Figure 19: Banks assessment scores for Processes criterion
Customer Results
153
52
86104
66
0
20
40
60
80
100
120
140
160
180
200
A B C D E
People Results
Figure 20 illustrates that banks B, C, and A were the best performing banks regarding this
criterion. They scored 82, 71, and 57 respectively. Bank E came next, scoring 35 points out
of 90. Bank D did especially poorly with only 14 points out of 90.
34
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Figure 20: Banks assessment scores for People Results criterion
People Results
57
71
35
14
82
0
10
20
30
40
50
60
70
80
90
A B C D E
Society Results
Figure 21 reveals another shocking finding, since participating banks obtained very weak
scores regarding the Society criterion. Bank B considered the best performing bank
regarding this criterion, reported only 29 points out of 60, while, the remaining scores
ranged from 9 to 20.
Figure 21 : Banks assessment scores for Society Results criterion
Society Results
29
19 20
10 9
0
10
20
30
40
50
60
A B C D E
35
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SERVICEEXCELLENCE:ANEMPIRICALSTUDYINTHESAUDIBANKINGINDUSTRY
Key Performance Results
The following figure 22 illustrates each banks score on the final excellence criterion i.e.
Key Performance Results. Banks C, B, and A were the top performing on this criterion; they
scored 140, 131.3 and 121 points respectively. On the other hand, banks D and E obtained
very disappointing scores of only 48.75 and 43 points out of 150.
Figure 22 Banks assessment scores for Key Performance Results criterion
Key Performance Results
121
43
140
48.75
131.3
10
30
50
70
90
110
130
150
A B C D E
Banks Total Assessment Scores
Figure 23 illustrates a marked contrast among the five participating banks. The red line
indicates the mean score of the total assessment scores for all banks. Bank B reported the
highest score, slightly less than 700 points. Banks A and C were very close to each other,
scoring 569 and 542 points respectively. Banks D and E were the only two banks to score
below the mean, they scored only 358 and 277 points.
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Figure 23: Banks total assessment scores
0
100
200
300
400
500
600
700Bank-A
Bank-B
Bank-CBank-D
Bank-E
Total ScoreMean score for all banks
Benefits of Benchmarking
Benchmarking can yield several valuable results. Finding out how well the best
organisations are currently performing helps in goal setting. Finding out who the best are
helps in determining productive benchmarking partner organisations. Finding out how the
best became that way often provides improvement ideas, ideas that can help an organisation
improve more efficiently. Analysis of the gaps from baseline (current performance level) to
benchmark (current performance level of the best companies) helps in prioritising resource
allocation. Simply analysing baseline and benchmark levels often helps an organisation
review and improves its system of measurements. Based on these facts about benchmarking,
the following sections will include some benchmarking activities and gap analysis.
Benchmarking the Aggregate Banks Scores
This section seeks to represent all five participating banks as one unit. In addition, it
includes a benchmarking exercise against the European Quality Association (EQA.), which
helps in understanding the precise excellence position of the participating banks, since this
was one of the studys objectives. The aggregate position of all five participated banks was
identified by calculating their mean scores on each criterion. Figure 24, presents the data. 37
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Figure 24: Aggregate score reported by participated banks
40.8047.40 49.01
52.40
39.80
51.80
17.40
96.8192.20
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00LE
AD
ERSH
IP
POLI
CY
&
STA
RTE
GY
PEO
PLE
MA
NA
GEM
ENT
PAR
TNER
SHIP
AN
D
RES
OU
RC
ES
PRO
CES
SES
CU
STO
MER
RES
ULT
S
PEO
PLE
RES
ULT
S
SOC
IETY
RES
ULT
S
KEY
PER
FOR
MA
NC
E
RES
ULT
S
Benchmarking Enablers and Results scoring ratio
Examining the ratio Enablers and Results scores to the overall score of each bank is a very
vital tool to assess the level of excellence within the origination. Excellent organisations
normally place more weight on the side of the Enablers rather than Results as a result of
focusing more on driving excellence. However, the participating banks derived more of their
total score on the results side, 52%, while only 48% of their reported points were on
Enablers criteria (see Figure 25).
Figure 25: Bank scores ratio on Enablers and Results criteria
Enablers, 48%
Results, 52%
Interestingly, in this regard, the European Quality Association (EQA) has offered a
benchmarking data which reveal that on average, excellent European organisations obtain 38
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SERVICEEXCELLENCE:ANEMPIRICALSTUDYINTHESAUDIBANKINGINDUSTRY
about 58% of their total assessment scores on Enablers criteria and only 42% on the Results
side (see Figure 26).
Figure 26: EQA scores ratio on Enablers and Results criteria
Enablers, 58%
Results, 42%
Figure 27 illustrates how the selected sample (i.e. the five Saudi banks) performed contrast
to the available EQA benchmark data. The blue bars represent the mean of all five banks for
each criterion, while the fluctuating red line signifies the EQA benchmarked data. Figure 27
figure provides a wider picture of the position of the five banks. Again the blue bars
represent the mean scores for the five banks for each criterion, while the red line in the chart
represents the mean benchmarking data provided by the EQA.
Benchmarking each criterion
Figure 27: Banks mean scores vs. the EQA median scores
47.40 49.0152.40 51.80
17.40
39.8040.80
96.8192.20
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
LEA
DE
RS
HIP
PO
LIC
Y &
STA
RTE
GY
PE
OP
LE
MA
NA
GE
ME
NT
PA
RTN
ER
SH
IPA
ND
RE
SO
UR
CE
S
PR
OC
ES
SE
S
CU
STO
ME
RR
ES
ULT
S
PE
OP
LER
ES
ULT
S
SO
CIE
TYR
ES
ULT
S
KE
YP
ER
FOR
MA
NC
ER
ES
ULT
S
Banks' Mean EQA Median Scores
The above figure reveals that all five Saudi banks almost meet the average EQA scores on
the three enablers criteria namely, Policy & Strategy, People Management, and Partnership 39
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40
and Resources. However, they fall short of achieving the average EQA scores on the
Leadership and Processes criteria. In the case of the Results criteria a bit more variation is
apparent between the five banks mean scores and the EQA benchmark score. On the
Customer, People, and Key Performance Results criteria, the five banks as one component
performed much better than the average of the EQA. Nonetheless, they reported a
significant failure regarding the Society Results criterion.
Gap Analysis
The next Table 13 illustrates the finding of a gap analyses conducted between the averages
of the EQA against the mean of the five participated banks in all criteria. This test will
clarify the aggregate banks performance more accurately.
Table 12: Gap analysis Enablers Results
LEA
DER
SHIP
PO
LIC
Y &
STR
ATE
GY
PEO
PLE
MA
NA
GEM
ENT
PA
RTN
ERSH
IP A
ND
R
ESO
UR
CES
PR
OC
ESSE
S
CU
STO
MER
RES
ULT
S
PEO
PLE
RES
ULT
S
SOC
IETY
RES
ULT
S
KEY
PER
FOR
MA
NC
E R
ESU
LTS
Tota
l Sco
re
Banks' mean Scores 40.80 47.40 49.01 52.40 39.80 92.20 51.80 17.40 96.81 487.62 EQA Median Scores 46 48 49 51 47 45 40 40 48 414
Total Gap -5.20 -0.60 0.01 1.40 -7.20 47.20 11.80 -22.60 48.81 73.616
Noticeably, the numbers within the last row, Total Gap varies in colour. The red coloured
numbers indicate a negative gap, signifying the failure of the five banks to achieve the
median score of the EQA on that particular criterion. On the other hand, numbers in green
signify a positive gap, in favour of the five banks. In order to achieve a better depiction of
the above gap analysis, it is possible to represent the above table in a different way through
the next figure (28).
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Figure 28 :Gap analysis
48
-7-0.6 -22
1 11470
-5
-25.00
-15.00
-5.00
5.00
15.00
25.00
35.00
45.00LE
ADER
SHIP
POLI
CY
&ST
ARTE
GY
PEO
PLE
MAN
AGEM
ENT
PAR
TNER
SHIP
AND
RES
OU
RC
ES
PRO
CES
SES
CU
STO
MER
RES
ULT
S
PEO
PLE
RES
ULT
S
SOC
IETY
RES
ULT
S
KEY
PER
FOR
MAN
CE
RES
ULT
S
Generally, the above figure reveals that the five banks failed to achieve the median score
reported by the EQA in all Enablers criteria, except for a very small advantage on the
Partnership and Resources management. The biggest negative gap among the Enables
criteria for Processes (-7.20), then Leadership (-5.20), and Policy and strategy with only -
.06. Meanwhile, the Partnership and Recourses management criterion showed the only
positive gap among all Enables; a positive score of 1.40. Although People management
showed a positive gap, it was so small (only 0.01) that can be considered as an equal score.
In contrast, the Gap analysis reveals that Results criteria reported healthier findings, with
three positive gaps out of four. Moreover, positive gaps here are greater in terms of size
compared to the gap size within the enablers. The highest positive gap was scored by Key
Performance Results (48.81), followed by Customer Results (47.20), and the last positive
gap was 11.80 for the People management criterion. The only negative gap reported in the
Results criteria was the Society result. However, it was the biggest negative gap among all
the nine criteria at -22.60.
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3.3 ANALYSISOFLAGGINGMEASURESLagging measures are used to provide confirmation of the conducted assessment technique
during this study. The latest six years from 2000 to 2006 post result lagging KPIs offers
the opportunity to investigate each banks performance in driving excellence. The chosen
KPIs in the following comparisons are: the market price of the banks Shears, Net profit,
Return on Assets (ROA), Owners Equity, and Return on Equity (ROE). In fact, the chosen
KPIs do not cover all the criteria identified within the EFQM excellence model. However, it
was decided to focus on these indicators, since they deal with the bottom line results.
Share Market Prices: considered one of the critical issues to any firms shareholders. All
five banks performed well regarding this issue.
Figure 29 Average annual growth of the banks share prices
45%38%
59%
26%
36%
0%
10%
20%
30%
40%
50%
60%
70%
A B C D E
Average growth in Share Market Price (%) Mean
As illustrated above in Figure 29, banks C and A were the top two banks, since both has
growth levels exceeding the banks average (41%). They reported an average annual growth
of 59% and 45% respectively. In contrast, bank B, D, and E experienced lower growth rates
38%, 26%, and 36% respectively.
Net Profit: Is another major indicator often referred to as the bottom line, Net profit is
calculated by subtracting a companys total expenses from total revenue, thus showing what
the company has earned (or lost) in a given period of time (usually one year).
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Figure 30: Average annual growth of the banks Net Profit
26%24%
30%
13%18%
0%
5%
10%
15%
20%
25%
30%
35%
A B C D EAverage growth in Net Profit (%) Mean
Figure 30 illustrates the average annual growth in Net Profits for the last six years. Banks A,
B, and C were the best performing banks from this angle. They reported an average yearly
growth of 26%, 24%, and 30% respectively. In contrast, both banks D and E performed less
than the group average; their growth rates were only 13% and 18% respectively.
Return on Assets (ROA): An indicator of how profitable a company is relative to its total
assets. ROA gives an idea how efficient management is at using its assets to generate
earnings. Calculated by dividing a companys annual earnings by its total assets, ROA is
displayed as a percentage. Sometimes this is referred to as "return on investment".
Figure 31: Average annual growth of the banks ROA
13%
9%
14%
13%
10%
5%
7%
9%
11%
13%
15%
A B C D E
Average of six years ROA Mean
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Figure 31 illustrates the position of the five banks according to this measure. Evidently,
Bank A confirmed its distinctiveness by reporting an average annual growth of ROA by
14%. Banks C and E came next each reporting a 13% level of growth. Banks B and D were
the least successful concerning this indicator, they report just 10% and 9% respectively.
Owners' Equity: The portion of the balance sheet that represents the capital received from
investors in exchange for stock (paid-in capital), donated capital and retained earnings.
Sometimes called Stockholders equity. It represents the equity stake currently held on the
books by a firm's equity investors .
Figure 32:Average annual growth of the banks Owners Equity
15% 15%16%
7% 6%
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
A B C D E
Average growth in Owners' Equity (%) Mean
The former Figure 32 shows as usual the high performance achieved by banks A, B, and C;
they performed better than the groups average (12%), attaining an average annual growth
on owners Equity of 15%, 15%, and 16% respectively. In contrast, banks D and E
presented weak results, with growth of only 7% and 6 % respectively.
Return on Equity: A measure of a corporations profitability, calculated as: Net income/
Owners' Equity. Basically, ROE reveals how much profit a company generates with the
money shareholders have invested in it. According to Investopedia (2006) the ROE is
useful for comparing the profitability of a company to that of other firms in the same
industry.
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Figure 33 Average annual growth of the banks ROE
24.57
29.09
24.5423.50
18.37
-
5.00
10.00
15.00
20.00
25.00
30.00
35.00
A B C D E
Average of six years ROE Mean
Figure 33 shows the leading position of bank C, since it reported the highest growth level,
29.09 %, making it the only bank that far exceeded the average growth level of 24%.
Moreover the figure reveals the similarity between banks A, B, and D, which reported
24.57%, 23.50%, and 24.54% respectively, just meeting the average level. Bank E as usual
was the weakest performing bank, with growth of only 18.37 on the ROE ratio.
3.4 HYPOTHESESTESTINGOnce having established the reliability and validity of the instruments used in the study, the
relationships between the constructs were investigated using Pearson Correlation and
Multiple Regression analysis. The following subsections present the results of testing all
hypotheses in this study.
Pearson Correlation Matrix
Pearson Correlations (r) range from 1 to +1. At these extremes, the correlation between the
two variables is perfect, although it is negative or inverse in the first instance. A perfect
correlation is one where the two variables increase or decrease by the same amount when r
=1.
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A correlation coefficient of 0 therefore refers to a situation where no relationship exists
between the two characteristics. In other words, changes in one variable cannot be explained
by the changes occurring in the second variable. After defining whether the correlation is
positive or negative based on the scores value, a common way of interpreting the strength
of a correlation is as illustrated in Table 14.
Table 13: Defining the correlation strength or weakness
R value
(+ or -) Meaning
0 - .1 = Negligible
.2 -.4 = Weak
.4 -.6 = Moderate
.6 -.8= Strong
.8 -.1= Very Strong
A commonly applied analysis to data that involve several variables is to run a Pearson
Correlation Matrix of all variables and then examine it for expected (and unexpected)
significant relations. The Pearson correlation coefficient was used to measure the strength of
a linear relationship among variables. Thus, measuring each correlation significant is an
important analysis to clarify the level of significance of correlations.
Hence, to achieve the study objectives a Pearson Correlation Matrix was first provided to
investigate the correlation among all main and sub-variables (i.e. the component of the
CEFs) as illustrated in Table 15. Afterwards, another Pearson Correlation Matrix was
offered only between the main tested independent and dependent variables (see Table 15).
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Table 14: Pearson Correlation Matrix among all variables
CEFs Enablers Results
LEA
DER
SHIP
PO
LIC
Y &
STR
ATE
GY
PEO
PLE
MA
NA
GEM
ENT
PA
RTN
ERSH
IP A
ND
R
ESO
UR
CES
PR
OC
ESSE
S
CU
STO
MER
RES
ULT
S
PEO
PLE
RES
ULT
S
SOC
IETY
RES
ULT
S
KEY
PER
FOR
MA
NC
E R
ESU
LTS
Cu
stom
ers
Per
cept
ion
SE
RV
QU
AL
Gap
LEADERSHIP 1.000 POLICY &STRATEGY .816** 1.000 PEOPLE MANAGEMENT .745* .834** 1.000 PARTNERSHIP AND RESOURCES .426 .848** .501 1.000
PROCESSES .519 .506 .326 .702* 1.000 CUSTOMER RESULTS .773* .837** .746* .441 .502 1.000 PEOPLE RESULTS .763* .608 .865** .394 .463 .588 1.000 SOCIETY RESULTS .230 .166 .099 .225 .301 .310 .336 1.000
CEF
s
KEY PERFORMANCE RESULTS .847** .765* .698 .479 .576 .502 .338 -.030 1.000
Customers Perception SERVQUAL Gap .661 .717* .846** .441 .702* .748* .831** .246 .420 1.000 * Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
Table 16 shows that almost all variables are positively correlated, since r values in most
cases are in positive, except in the case of the Society and Key performance results.
Nevertheless, not every identified positive correlation was significant. The researcher will
only consider coefficients with values above .40. This means that only moderate, strong, and
very strong correlations will be accepted in this study.
The Society results criterion was the only element that seemed to have a less interaction
with other variables, since, it was Society positively correlated with only four variables;
Process, Customers results, People results, and Key performance results. In fact the r values
of these correlations were very weak .301, .310, .336, and .338) respectively.
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Table 15: Pearson Correlation Matrix for the tested variables
Enablers Criteria CEFs
SERVQUAL Gap
Customer Results
Key Performance
Results Enablers Criteria CEFs
Pearson Correlation 1
Sig. (2-tailed) . SERVQUAL Gap
Pearson Correlation .729* 1
Sig. (2-tailed) .017 . Customer Results
Pearson Correlation .577 .748* 1
Sig. (2-tailed) .081 .013 . Key Performance Results
Pearson Correlation .877** .420 .502 1
Sig. (2-tailed) .001 .227 .139 . * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
According to the correlation matrix and the coefficient records shown in Table 16 it was
apparent that all of the correlation coefficients among the main variables were larger than .3;
this indicated that there were a significant correlations among all variables included in the
three suggested hypotheses. In other words, a bank, which had a good CEFs score, tended to
achieve better customers perception (r = .729, p< .005). Furthermore, the correlation matrix
indicates that a significant correlation existed between CEFs and Key performance results
(r=0.877; p
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SERVQUAL gap scores were taken as the dependent variable and several tables were
generated, containing t